Feasibility of Long-Term Physical Activity Measurement With a Wearable Activity Tracker in Patients With Axial Spondyloarthritis: 1-Year Longitudinal Observational Study
Bibliographic record
Abstract
Background: Using wearable activity trackers shows promise in measuring physical activity in patients with axial spondyloarthritis (axSpA). However, little is known regarding the feasibility of long-term use. objectives: This study aimed to explore the feasibility of recording physical activity using a wearable activity tracker and describe wear-time patterns among patients with axSpA. Methods: Data from a randomized controlled trial (NCT: 05031767) were analyzed. Patients with axSpA and low disease activity were recruited from an outpatient clinic and asked to wear a Garmin vívosmart 4 activity tracker for 1 year. The activity tracker measured steps and heart rate. Trial feasibility (eligibility, inclusion rate, and patient characteristics), technical feasibility (data recorded, tracker adherence, ie, days worn, and missing data), and operational feasibility (synchronization reminders and tracker replacements) were analyzed. Tracker adherence was calculated as the percentage of recorded minutes of the maximum possible minutes. Unsupervised hierarchical clustering was used to explore tracker wear-time patterns. Results: Of the 160 patients screened, 75 (47%) agreed to use the tracker and 64 (85%) were analyzed (11 had insufficient data). The median activity tracker adherence over 1 year was 66% (IQR 30-86). There was 30% missing step and 0.01% heart rate data in the physical activity dataset. A median of 18 (IQR 9-25) reminders per patient to synchronize activity data were distributed. Analysis of wear-time patterns resulted in 3 groups: Adherent (33/64, 51% of patients), Minimal Use (17/64, 27%), and Intermittently adherent (14/64, 22%). Conclusions: Trial feasibility was low, while technical and operational feasibility were acceptable. Only 51% of the patients were highly adherent. Activity trackers, though trendy, have low to moderate feasibility over 1 year in patients with axSpA. Automated synchronization and adherence barriers should be further explored.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".